Book Image

Data Science with Python

By : Rohan Chopra, Aaron England, Mohamed Noordeen Alaudeen
Book Image

Data Science with Python

By: Rohan Chopra, Aaron England, Mohamed Noordeen Alaudeen

Overview of this book

Data Science with Python begins by introducing you to data science and teaches you to install the packages you need to create a data science coding environment. You will learn three major techniques in machine learning: unsupervised learning, supervised learning, and reinforcement learning. You will also explore basic classification and regression techniques, such as support vector machines, decision trees, and logistic regression. As you make your way through the book, you will understand the basic functions, data structures, and syntax of the Python language that are used to handle large datasets with ease. You will learn about NumPy and pandas libraries for matrix calculations and data manipulation, discover how to use Matplotlib to create highly customizable visualizations, and apply the boosting algorithm XGBoost to make predictions. In the concluding chapters, you will explore convolutional neural networks (CNNs), deep learning algorithms used to predict what is in an image. You will also understand how to feed human sentences to a neural network, make the model process contextual information, and create human language processing systems to predict the outcome. By the end of this book, you will be able to understand and implement any new data science algorithm and have the confidence to experiment with tools or libraries other than those covered in the book.

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Table of Contents (10 chapters)
About the Book
About the Book
About the Authors
Learning Objectives
Audience
Approach
Minimum Hardware Requirements
Software Requirements
Installation and Setup
Using Kaggle for Faster Experimentation
Conventions
Installing the Code Bundle
Free Chapter
1
Introduction to Data Science and Data Pre-Processing
Introduction to Data Science and Data Pre-Processing
Introduction
Python Libraries
Roadmap for Building Machine Learning Models
Data Representation
Data Cleaning
Data Integration
Data Transformation
Data in Different Scales
Data Discretization
Train and Test Data
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Performance Metrics
Summary
2
Data Visualization
Data Visualization
Introduction
Functional Approach
Object-Oriented Approach Using Subplots
Summary
3
Introduction to Machine Learning via Scikit-Learn
Introduction to Machine Learning via Scikit-Learn
Introduction
Introduction to Linear and Logistic Regression
Multiple Linear Regression
Logistic Regression
Max Margin Classification Using SVMs
Decision Trees
Random Forests
Summary
4
Dimensionality Reduction and Unsupervised Learning
Dimensionality Reduction and Unsupervised Learning
Introduction
Hierarchical Cluster Analysis (HCA)
K-means Clustering
Principal Component Analysis (PCA)
Supervised Data Compression using Linear Discriminant Analysis (LDA)
Summary
5
Mastering Structured Data
Mastering Structured Data
Introduction
Boosting Algorithms
XGBoost Library
External Memory Usage
Cross-validation
Saving and Loading a Model
Neural Networks
Keras
Categorical Variables
Summary
6
Decoding Images
Decoding Images
Introduction
Images
Convolutional Neural Networks
Pooling Layer
Adam Optimizer
Cross-entropy Loss
Regularization
Image Data Preprocessing
Data Augmentation
Generators
Summary
7
Processing Human Language
Processing Human Language
Introduction
Text Data Processing
Recurrent Neural Networks (RNNs)
Summary
8
Tips and Tricks of the Trade
Tips and Tricks of the Trade
Introduction
Transfer Learning
Useful Tools and Tips
AutoML
Summary
Appendix
Appendix
Chapter 1: Introduction to Data Science and Data Preprocessing
Chapter 2: Data Visualization
Chapter 3: Introduction to Machine Learning via Scikit-Learn
Chapter 4: Dimensionality Reduction and Unsupervised Learning
Chapter 5: Mastering Structured Data
Chapter 6: Decoding Images
Chapter 7: Processing Human Language
Chapter 8: Tips and Tricks of the Trade

Roadmap for Building Machine Learning Models

The roadmap for building machine learning models is straightforward and consists of five major steps, which are explained here:

  • Data Pre-processing

    This is the first step in building a machine learning model. Data pre-processing refers to the transformation of data before feeding it into the model. It deals with the techniques that are used to convert unusable raw data into clean reliable data.

    Since data collection is often not performed in a controlled manner, raw data often contains outliers (for example, age = 120), nonsensical data combinations (for example, model: bicycle, type: 4-wheeler), missing values, scale problems, and so on. Because of this, raw data cannot be fed into a machine learning model because it might compromise the quality of the results. As such, this is the most important step in the process of data science.

  • Model Learning

    After pre-processing the data and splitting it into train/test sets (more on this later), we move on to modeling. Models are nothing but sets of well-defined methods called algorithms that use pre-processed data to learn patterns, which can later be used to make predictions. There are different types of learning algorithms, including supervised, semi-supervised, unsupervised, and reinforcement learning. These will be discussed later.

  • Model Evaluation

    In this stage, the models are evaluated with the help of specific performance metrics. With these metrics, we can go on to tune the hyperparameters of a model in order to improve it. This process is called hyperparameter optimization. We will repeat this step until we are satisfied with the performance.

  • Prediction

    Once we are happy with the results from the evaluation step, we will then move on to predictions. Predictions are made by the trained model when it is exposed to a new dataset. In a business setting, these predictions can be shared with decision makers to make effective business choices.

  • Model Deployment

    The whole process of machine learning does not just stop with model building and prediction. It also involves making use of the model to build an application with the new data. Depending on the business requirements, the deployment may be a report, or it may be some repetitive data science steps that are to be executed. After deployment, a model needs proper management and maintenance at regular intervals to keep it up and running.

This chapter will mainly focus on pre-processing. We will cover the different tasks involved in data pre-processing, such as data representation, data cleaning, and others.

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